Study On Pressing Sintering Molding And Process Optimization Of Metal Plastic Self-lubricating Composite

Metal plastic self-lubricating composite is a kind of composite material with self lubrication,wear resistance,impact resistance and light weight.It has been widely used.In the actual production process,the molding technology of products has the most direct impact on their comprehensive properties.The purpose of this study is to form a metal plastic self lubrication composite material with high strength and excellent friction and wear properties,and study the influence of pressing sintering process parameters on product quality evaluation index,and optimize the molding process.The main contents and results of this paper are as follows:(1)The type of sintering of metal-plastic self-lubricating composite materials and the sintering characteristics of each sintering stage were theoretically studied.The influence of temperature,pressure,and time process parameters on the properties of the product during the press-sintering process was analyzed to determine the product sintering molding process parameter range,in order to provide theoretical guidance for the molding experiments.(2)Combining orthogonal test and numerical analysis methods,the factors and evaluation indexes involved in the optimization of press sintering process parameters of metal-plastic self-lubricating composite materials were introduced.The molding temperature,molding pressure,molding time,and temperature increase rate were used to study the bonding strength,friction coefficient and wear resistance of the products.and the sensitivity of the influence of process parameters on the evaluation index was determined through analysis,and the corresponding influence map of the factor level was obtained,and the three sets of process parameters combinations were determined when the three performance evaluation indexes were respectively optimized.Using the fuzzy mathematics comprehensive evaluation method,a number of objectives were converted into a single target,and a mathematic evaluation model for the comprehensive performance of the products corresponding to multiple indicators was established.The process parameter combinations were obtained when the overall product performance was optimal within the selected process parameters,and the results of the optimization were tested and analyzed by experiment.(3)Using the predictability of BP neural network,Matlab software was used to simulate and optimize.The four process parameters of molding temperature,molding pressure,molding time,and heating rate were used as input factors to combine strength,friction coefficient,and wear resistance.The performance evaluation indicators were output parameters,and the corresponding BP neural network was established.The training learning and simulation were performed.The obtained model was tested and it was concluded that the network model could predict the product performance evaluation index values under different combinations of process parameters.Finally,the four fitting function equations between the process parameters and the bonding strength,the friction coefficient,the wear resistance and the comprehensive evaluation were obtained by using the numerical fitting method,and the fitting error was analyzed,and the combination of process parameters with the highest score of comprehensive evaluation is predicted.(4)The nature of the experimental equipment and experimental raw materials was analyzed,the experimental scheme and the forming process steps were determined,the materials were pretreated,and the press-sintering molds were designed,Under different combinations of process parameters,press and sinter molding experiments are carried out according to the orthogonal test scheme.The performance of the products obtained by molding was tested.The results show that:by comparing the process optimization results with experimental test results,it is concluded that the established BP neural network model has a good agreement with the experimental test values,This shows that the network model can be used to predict the product performance evaluation index values under different combinations of process parameters.The numerical fitting results show that the molding temperature ranges from 332.32~oC to 348.04~oC,the molding pressure range from 9.39MPa to 9.84MPa,the molding time range from48.87min to 51.18min,and the heating rate range from 5.86~oC/min to 6.14~o C/min.The synthetic properties of metal-plastic self-lubricating composites formed within this process parameter range are the best.